Chapter 36 [fMRI] ROI ~ cue * stim

What is the purpose of this notebook?

Here, I model NPS dot products as a function of cue, stimulus intensity and expectation ratings.
One of the findings is that low cues lead to higher NPS dotproducts in the high intensity group, and that this effect becomes non-significant across sessions.
03/23/2023: For now, I’m grabbing participants that have complete data, i.e. 18 runs, all three sessions.

36.1 common parameters

main_dir = dirname(dirname(getwd()))
print(main_dir)

## [1] "/Users/h/Dropbox (Dartmouth College)/projects_dropbox/social_influence_analysis"

exclude_list <- "sub-0001|sub-0002|sub-0003|sub-0004|sub-0005|sub-0007|sub-0008|sub-0013|sub-0016|sub-0017|sub-0019|sub-0020|sub-0021|sub-0025|sub-0075"

## load brain extracted values {.unlisted .unnumbered}
extraction_fname = '/Users/h/Dropbox/projects_dropbox/social_influence_analysis/analysis/fmri/nilearn/signature_canlabcore/roi-painpathway_sub-all_runtype-pvc_event-stimulus.csv'
brain.df <- load_extraction(fname = extraction_fname)
head(brain.df)

##   Thal_VPLM_R Thal_VPLM_L     Thal_MD    dpIns_L     dpIns_R   aMCC_MPFC
## 1 -0.40824360  0.05623318  0.39523301 -0.3265142 -0.23427893 -0.35453591
## 2  0.61643428  1.45565188  2.63185191  1.1359142  1.10305393  0.84601259
## 3  0.16190505 -0.51763445 -0.21110538 -0.2966219 -0.34322661 -0.68279624
## 4  0.27362254  0.24603392  1.40747046  0.2938125  0.10848080  0.31231993
## 5 -0.09862687  0.38056991  1.43355429  0.2300755 -0.06363615  0.06286529
## 6  0.07230759  0.23984034  0.06749836  0.3386498  0.04078389  0.02369536
##        sub    ses    run           runtype      task          event     trial
## 1 sub-0002 ses-03 run-01 runtype-vicarious vicarious event-stimulus trial-000
## 2 sub-0002 ses-03 run-01 runtype-vicarious vicarious event-stimulus trial-001
## 3 sub-0002 ses-03 run-01 runtype-vicarious vicarious event-stimulus trial-002
## 4 sub-0002 ses-03 run-01 runtype-vicarious vicarious event-stimulus trial-003
## 5 sub-0002 ses-03 run-01 runtype-vicarious vicarious event-stimulus trial-004
## 6 sub-0002 ses-03 run-01 runtype-vicarious vicarious event-stimulus trial-005
##        cuetype stimintensity
## 1  cuetype-low           low
## 2 cuetype-high           med
## 3 cuetype-high           low
## 4 cuetype-high          high
## 5  cuetype-low           med
## 6  cuetype-low          high

## load behavioral data {.unlisted .unnumbered}
beh.df <- load_pvc_beh(datadir = file.path(main_dir, 'data', 'beh', 'beh02_preproc'),
             subject_varkey = "src_subject_id",
             iv = "param_stimulus_type",
             dv = "event04_actual_angle", 
             exclude = exclude_list)

merge two dataframes

beh.df.sub <- beh.df[,c("sub", "ses", "run", "runtype", "trial", "cuetype", "stimintensity","event02_expect_angle", "event04_actual_angle")]
pvc <- merge(brain.df, beh.df.sub, 
                  by.x = c("sub", "ses", "run", "runtype", "trial", "cuetype", "stimintensity"),
                  by.y = c("sub", "ses", "run", "runtype", "trial", "cuetype", "stimintensity")
                  )
pvc.df <- simple_contrast_singletrial(pvc)

36.2 plot task x intensity

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36.3 plot cue x intensity

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## [1] "stim_ordered"

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